mlsanityData to 14 Oct 2026

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models

arXiv 2504.17210 ↗First posted 24 Apr 2025cs.LG cs.AIcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Many data-driven modules in smart grid rely on access to high-quality power flow data; however, real-world data are often limited due to privacy and operational constraints. This paper presents a physics-informed generative framework based on Denoising Diffusion Probabilistic Models (DDPMs) for synthesizing feasible power flow data. By incorporating auxiliary training and physics-informed loss functions, the proposed method ensures that the generated data exhibit both statistical fidelity and adherence to power system feasibility. We evaluate the approach on the IEEE 14-bus and 30-bus benchmark systems, demonstrating its ability to capture key distributional properties and generalize to out-of-distribution scenarios. Comparative results show that the proposed model outperforms three baseline models in terms of feasibility, diversity, and accuracy of statistical features. This work highlights the potential of integrating generative modelling into data-driven power system applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 13 of its 25 references that are in the map. First posted on arXiv 24 Apr 2025.

Builds on

13 of 25 references.

References · 13

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  12. 2021Synthetic Time-Series Load Data via Conditional Generative Adversarial Networkscited by 3
1 more
  1. 2024Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrationscited by 3

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  5. 2026Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
  6. 2023Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models
  7. 2025A synthetic dataset of French electric load curves with temperature conditioning
  8. 2023Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties